I am attempting to trim Illumina RNA-seq data (paired-end) I downloaded from NCBI in SRA format. I have converted the .sra file into two .fastq using fastQ-dump. I then FastQC'd both files, which indicated there were adapters/primers present:

I have been told to use Trimmomatic, but I am struggling getting it to work. Could someone please guide as to how to only remove the above sequences (no quality trimming etc)? i.e Only run the ILLUMINACLIP process and how to specify a custom list of adapters/primers to be trimmed.

Also, will Trimmomatic work on 454 data? If not, what would be a suitable alternative?

fastaWithAdaptersEtc: specifies the path to a fasta file containing all the adapters, PCR sequences etc. The naming of the various sequences within this file determines how they are used. See below.

seedMismatches: specifies the maximum mismatch count which will still allow a full match to be performed

palindromeClipThreshold: specifies how accurate the match between the two 'adapter ligated' reads must be for PE palindrome read alignment.

simpleClipThreshold: specifies how accurate the match between any adapter etc. sequence must be against a read.

and here's the relevant portion from the "see below" reference:

To make a custom version of fasta, you must first understand how it will be used. Trimmomatic uses two strategies for adapter trimming: Palindrome and Simple

With 'simple' trimming, each adapter sequence is tested against the reads, and if a sufficiently accurate match is detected, the read is clipped appropriately.

'Palindrome' trimming is specifically designed for the case of 'reading through' a short fragment into the adapter sequence on the other end. In this approach, the appropriate adapter sequences are 'in silico ligated' onto the start of the reads, and the combined adapter+read sequences, forward and reverse are aligned. If they align in a manner which indicates 'read-through', the forward read is clipped and the reverse read dropped (since it contains no new data).

Naming of the sequences indicates how they should be used. For 'Palindrome' clipping, the sequence names should both start with 'Prefix', and end in '/1' for the forward adapter and '/2' for the reverse adapter. All other sequences are checked using 'simple' mode. Sequences with names ending in '/1' or '/2' will be checked only against the forward or reverse read. Sequences not ending in '/1' or '/2' will be checked against both the forward and reverse read. If you want to check for the reverse-complement of a specific sequence, you need to specifically include the reverse-complemented form of the sequence as well, with another name.

Personally, I recommend cutadapt, followed by quality trim with sickle. If you hit a wall with trimmomatic, perhaps cutadapt is worth a look?

EDIT: to answer your latter question, I don't see why trimmomatic wouldn't work with 454 data in FASTQ format, as long as you correctly specify your adapters.

"- seedMismatches: specifies the maximum mismatch count which will still allow a full match to be performed
- palindromeClipThreshold: specifies how accurate the match between the two 'adapter ligated' reads must be for PE palindrome read alignment.
- simpleClipThreshold: specifies how accurate the match between any adapter etc. sequence must be against a read.

The thresholds used are a simplified log-likelihood approach. Each matching base adds just over 0.6, while each mismatch reduces the alignment score by Q/10. Therefore, a perfect match of a 12 base sequence will score just over 7, while 25 bases are needed to score 15. As such we recommend values between 7 - 15 for this parameter. For palindromic matches, a longer alignment is possible - therefore this threshold can be higher, in the range of 30. The 'seed mismatch' parameter is used to make alignments more efficient, specifying the maximum base mismatch count in the 'seed' (16 bases). Typical values here are 1 or 2."

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Hope this helps. Trimmomatic seems to be one of these programs that requires about 10 tries before it runs successfully. The values (2:30:10) are given as examples on the trimmomatic website and have worked well for my data. Slight tweaking might be required depending on your needs.

Following a discussion with my colleagues who have tested these trimming tools against data for which they know the truth, they advise using trimmomatic for quality trimming and cutadapt for adapter trimming. Trimmomatic seems to not fully remove adapters in some cases. I'd advise trying all combinations of approaches on your datasets to work out which is best.